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In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

The purpose of this paper is to focus on challenges faced by longitudinal quantitative analyses of school improvement processes and offers a systematic literature review of current papers that use longitudinal analyses. In this context, the authors assessed designs and methods that are used to analyze the relation between school improvement processes and student outcomes. Based on this the authors point out to what extent the papers consider different aspects of the complex nature of school improvement (e.g. multilevel structure, indirect and nonlinear effects, reciprocity). The choice of study designs and methods of analysis substantially determines which aspects of this complexity are taken into account.

The authors searched in four international high-impact journals and in ERIC for articles reporting longitudinal school improvement studies. The database of the review consisted of a total of 428 journal articles. In total, 13 of the 428 papers met the selection criteria and were analyzed in detail.

The analyzed papers use a wide range of designs and methodological approaches. They support the assumption that sophisticated quantitative longitudinal designs and methods can be applied effectively in school improvement research. However, considering the complexity of school improvement is accompanied by high demands on designs and methods. Due to this none of the papers met the standards applied in this review completely.

In particular, further research is needed to consider a long period of observation, reciprocal indirect and nonlinear processes in a multilevel structure. Moreover, research is required for a better and unambiguous theoretical foundation and empirical validation of the number of and intervals between measurement points.

If more consideration is given to the complex nature of school improvement in future studies, the broader knowledge base will allow a better understanding of the dynamic relation of school improvement and student learning. It would thus be possible to make more appropriate recommendations for the support of school improvement practice.

The original contribution of the paper is to show which aspects of the complexity of school improvement processes – and to what extent – are currently addressed in designs and methods of analysis applied in quantitative longitudinal studies that investigate the relation between schools’ capacity to managing change and student outcomes. Additionally the authors aim at deriving need for further research and giving guidelines how designs and methods in further studies can reflect the complexity appropriately. It is highly important to consider all aspects of this complexity to describe and understand the dynamic relation of school improvement processes and student outcomes.

The purpose of this paper is to provide a systematic overview with guidelines how to use partial least squares (PLS) path modeling in longitudinal studies. Practical examples from a study of the acceptance of battery electric vehicles (BEVs) in corporate fleets are used for demonstration purposes.

In this study, data at three points in time were collected: before the initial use of a BEV, after three and after six months of extensive usage of BEVs.

Three different models are identified depending on the research objective and on the data basis. Multigroup analyses are suggested to test the difference between the path coefficients of latent variables at different points in time. Limitations for the use of repeated cross-sectional data have to be observed.

Academics and practitioners will benefit from this paper by receiving an overview of the different PLS path models in longitudinal studies. A decision-tree enables them to make a choice regarding the most appropriate model and suggests a sequence of complementary analyses. So far, there is a lack of a tutorial type paper delivering such guidance.

Economists and sociologists have proposed arguments for why there can exist wage penalties for work involving helping and caring for others, penalties borne disproportionately by women. Evidence on wage penalties is neither abundant nor compelling. We examine wage differentials associated with caring jobs using multiple years of Current Population Survey (CPS) earnings files matched to O*NET job descriptors that provide continuous measures of “assisting & caring” and “concern” for others across all occupations. This approach differs from prior studies that assume occupations either do or do not require a high level of caring. Cross-section and longitudinal analyses are used to examine wage differences associated with the level of caring, conditioned on worker, location, and job attributes. Wage level estimates suggest substantive caring penalties, particularly among men. Longitudinal estimates based on wage changes among job switchers indicate smaller wage penalties, our preferred estimate being a 2% wage penalty resulting from a one standard deviation increase in our caring index. We find little difference in caring wage gaps across the earnings distribution. Measuring mean levels of caring across the U.S. labor market over nearly thirty years, we find a steady upward trend, but overall changes are small and there is no evidence of convergence between women and men.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

To review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

The method applied to carry out the study of the transformation of strategic alliances in emerging markets is discussed in this chapter. Specifically, this chapter discusses how the whole study is conducted, i.e. the identification and development of the research issues, the research aim and problem, the development of the conceptual framework, the development of the research method, how the empirical study is analyzed and conclusions are drawn. The specific research strategy applied to conduct the current study is a longitudinal research approach. A short discussion on how the ECE countries are classified into various groups is conducted in the current chapter and a detailed discussion on the classification of ECE countries into different groups is also conducted in chapter eight. Moreover, the logic for adopting a longitudinal research strategy to conduct the current study of the transformation of strategic alliances is also justified. The identification and choice of the case companies, the logic followed in the development of the questionnaire and how much and to what extent the questionnaire is modified are also discussed in detail. The identification of the interviewees, the execution of the interviews and the transcription of the interview materials and the structuring of the cases are also discussed in detail.

The purpose of this paper is to provide evidence that the U‐shaped relationship between intellectual property rights (IPRs) and per capita gross domestic product (GDP) observed in the past literature using a panel of data is not a consequence of longitudinal forces, as has been previously postulated, but instead a consequence of cross‐sectional influences.

Differences in the longitudinal and cross‐sectional relationship between IPRs and per capita GDP are analyzed through a variety of methods, including pooled regression analysis that isolates the regional differences that are critical in making an accurate longitudinal analysis from the panel data.

Analyzing the country data reveals that a longitudinal U‐shaped relationship is counterfactual, as countries generally do not weaken their IPRs once they are in place, barring a regime change or other alteration in their political economy. The significant U‐shape link between IPRs and per capita GDP empirically observed in preliminary analysis of the panel data is instead a result of cross‐sectional influences.

Making the distinction between the cross‐sectional and longitudinal relationship between IPRs and per capita GDP provides a more accurate insight about how IPRs change in a country as it develops.

This paper aims to propose a method for the longitudinal analysis of the environment considering both firms' and environmental variables.

The study is based on a sample of firms in Canary Islands (Spain) for 2000 and 2003. Managerial perceptions are considered, based on the cognitive perspective. The measurements used are the result of applying the Rasch model and the rack and stack analyses. This approach provides information about how dynamic the firms perceive the environment and also about how the items are perceived.

The results show that most firms perceive that dynamism increased between 2000 and 2003. From the perspective of the environmental variables, the most dynamic are perceived to be competition, demand, consumer motivation and technological resources.

This paper proposes a longitudinal method for environmental scanning that include both firms' and environmental variables. It considers managerial perceptions, that is the information entering the decision making process. It is one of the first papers to study environmental scanning with Rasch model and one of the few about longitudinal environmental analyses. It opens a field of research and applications of the Rasch model in the management literature.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.